This is a revision of a grant application originally reviewed in July of 1999. Studies document that it takes an average of nearly two decades for research evidence to reach clinical practice. Relying on the passive diffusion of information to keep health professionals' knowledge up to date is doomed to failure in a global environment in which about two million medical research articles are published annually. The purpose of this revised project is to: (1) develop policies for the automated selection of credible and substantial evidence, and (2) match patient data with clinical evidence and (3) directly deliver high-quality evidence to the point of clinical decision making. The proposed project is a collaboration of researchers from the University of Missouri, Columbia University, and Duke University. Systematic reviews to evaluate eligibility criteria, outcomes variables, and overall quality of published evidence will be conducted. An automated evidence filtering system will be developed and selected randomized clinical trial evidence will be abstracted from the literature. The abstractions will contain eligibility criteria for matching, a recommendation to the clinician, and a suggested notice to the patient. A decision-support system to automatically match the literature with patient data will be created. Based on the abstracted literature evidence, an intelligent web-based agent will search the patient databases for lapses in care. Reminder recommendations and evidence behind them will be sent to the appropriate clinician. Reminder standards will be developed and evaluated with a randomized clinical trial. In addition there will be a subcontract to Duke University which will focus on developing a clinical decision support system that uses structures, electronically stored evidence logic modules (created by the other investigators in this project) to deliver evidence-based advice to clinicians. This decision support system will be evaluated in a randomized controlled trial that assesses the impact of the system on adherence with care standards.